Global summary

Identifying changes in the reproduction number, rate of spread, and doubling time during the course of the COVID-19 outbreak whilst accounting for potential biases due to delays in case reporting both nationally and subnationally.

Expected daily cases by country


Figure 1: The results of the latest reproduction number estimates (based on estimated cases with a date of infection on the 2020-03-22) can be summarised by whether cases are likely increasing or decreasing. This represents the strength of the evidence that the reproduction number in each region is greater than or less than 1, respectively. Countries are only included if at least 100 cases have been reported on a single day. Countries with fewer than 100 cases reported on a single day are not included in the analysis (light grey).

Summary of latest reproduction number and case count estimates by date of infection


Figure 1: Cases with date of infection on the 2020-03-22 and the time-varying estimate of the effective reproduction number (light bar = 90% credible interval; dark bar = the 50% credible interval.). Regions are ordered by the number of expected daily cases and shaded based on the expected change in daily cases. The dotted line indicates the target value of 1 for the effective reproduction no. required for control and a single case required for elimination.

Reproduction numbers over time in the six regions expected to have the most incident cases


Figure 2: Time-varying estimate of the effective reproduction number (light ribbon = 90% credible interval; dark ribbon = the 50% credible interval) in the regions expected to have the highest number of incident cases. Estimates are shown up to the 2020-03-22. Confidence in the estimated values is indicated by translucency with increased translucency corresponding to reduced confidence. The dotted line indicates the target value of 1 for the effective reproduction no. required for control.

Reported cases and their estimated date of infection in the six regions expected to have the most incident cases


Figure 3: Cases by date of report (bars) and their estimated date of infection (light ribbon = 90% credible interval; dark ribbon = the 50% credible interval) in the regions expected to have the highest number of incident cases. Estimates are shown up to the 2020-03-22. Confidence in the estimated values is indicated by translucency with increased translucency corresponding to reduced confidence.

Reproduction numbers over time in all regions


Figure 4: Time-varying estimate of the effective reproduction number (light ribbon = 90% credible interval; dark ribbon = the 50% credible interval) in all regions. Estimates are shown up to the 2020-03-22. Confidence in the estimated values is indicated by translucency with increased translucency corresponding to reduced confidence. The dotted line indicates the target value of 1 for the effective reproduction no. required for control.

Reported cases and their estimated date of infection in all regions

Figure 5: Cases by date of report (bars) and their estimated date of infection (light ribbon = 90% credible interval; dark ribbon = the 50% credible interval) in all regions. Estimates are shown up to the 2020-03-22. Confidence in the estimated values is indicated by translucency with increased translucency corresponding to reduced confidence.

Latest estimates (as of the 2020-03-22)

Table 1: Latest estimates (as of the 2020-03-22) of the number of cases by date of infection, the effective reproduction number, and the doubling time in each region. The mean and 90% credible interval is shown.
Country/Region New infections Expected change in daily cases Effective reproduction no. Doubling time (days)
Algeria 85 (21 – 152) Likely increasing 1.6 (0.8 – 2.3) 8.7 (3.3 – Cases decreasing)
Argentina 131 (43 – 203) Likely increasing 1.4 (0.9 – 1.9) 11 (4.3 – Cases decreasing)
Australia 429 (246 – 635) Unsure 1.1 (0.9 – 1.4) 46 (9.3 – Cases decreasing)
Austria 833 (445 – 1209) Unsure 1.1 (0.8 – 1.4) 43 (8.3 – Cases decreasing)
Belgium 1789 (931 – 2672) Increasing 1.6 (1 – 2.2) 6.9 (3.6 – 55)
Brazil 538 (253 – 811) Likely increasing 1.3 (0.8 – 1.8) 15 (5.4 – Cases decreasing)
Canada 1037 (546 – 1530) Increasing 1.4 (1 – 1.9) 8.4 (4.3 – 430)
Chile 363 (166 – 568) Likely increasing 1.5 (0.9 – 2.1) 8.1 (3.8 – Cases decreasing)
China 110 (62 – 151) Unsure 1.1 (0.8 – 1.5) 37 (8.6 – Cases decreasing)
Czech Republic 307 (137 – 454) Likely increasing 1.3 (0.8 – 1.7) 13 (4.6 – Cases decreasing)
Denmark 224 (98 – 331) Likely increasing 1.4 (0.9 – 2) 9.1 (4.2 – Cases decreasing)
Dominican Republic 168 (32 – 285) Likely increasing 1.6 (0.7 – 2.4) 8.6 (3.3 – Cases decreasing)
Ecuador 178 (76 – 272) Unsure 1.1 (0.7 – 1.5) 1300 (8.3 – Cases decreasing)
Estonia 68 (22 – 104) Likely increasing 1.4 (0.7 – 1.9) 10 (3.6 – Cases decreasing)
Finland 120 (21 – 197) Unsure 1.2 (0.7 – 1.7) 26 (5.3 – Cases decreasing)
France 4644 (2593 – 6943) Likely increasing 1.3 (1 – 1.8) 13 (5.7 – Cases decreasing)
Germany 5764 (3253 – 8030) Likely increasing 1.2 (0.9 – 1.6) 18 (6.7 – Cases decreasing)
Greece 104 (44 – 163) Likely increasing 1.3 (0.8 – 1.8) 16 (5.3 – Cases decreasing)
India 167 (82 – 270) Likely increasing 1.5 (0.9 – 2) 9.8 (4.4 – Cases decreasing)
Indonesia 183 (69 – 288) Likely increasing 1.4 (0.8 – 2) 12 (4.3 – Cases decreasing)
Iran 3556 (1759 – 5118) Likely increasing 1.4 (0.9 – 1.9) 9 (4.5 – Cases decreasing)
Ireland 353 (138 – 517) Likely increasing 1.4 (0.9 – 1.8) 12 (4.8 – Cases decreasing)
Israel 567 (208 – 826) Likely increasing 1.3 (0.8 – 1.9) 13 (4.9 – Cases decreasing)
Italy 6295 (3549 – 9060) Unsure 1.1 (0.8 – 1.4) 65 (9.3 – Cases decreasing)
Japan 164 (87 – 230) Increasing 1.5 (1 – 2) 7.6 (4.1 – 57)
Luxembourg 177 (46 – 286) Unsure 1.1 (0.6 – 1.5) 1400 (6.7 – Cases decreasing)
Malaysia 187 (103 – 268) Unsure 1.1 (0.8 – 1.4) 42 (8.5 – Cases decreasing)
Mexico 180 (68 – 283) Increasing 1.6 (0.9 – 2.3) 6.8 (3.3 – Cases decreasing)
Morocco 108 (24 – 180) Increasing 1.8 (1 – 2.6) 6 (2.8 – Cases decreasing)
Netherlands 1340 (699 – 1930) Likely increasing 1.4 (0.9 – 1.8) 11 (5 – Cases decreasing)
Norway 313 (156 – 479) Unsure 1.2 (0.7 – 1.5) 28 (6.4 – Cases decreasing)
Pakistan 167 (52 – 301) Unsure 1.3 (0.7 – 1.9) 19 (4.7 – Cases decreasing)
Panama 149 (47 – 237) Likely increasing 1.4 (0.8 – 1.9) 11 (4.1 – Cases decreasing)
Peru 138 (35 – 231) Likely increasing 1.6 (0.7 – 2.3) 7.7 (3.2 – Cases decreasing)
Philippines 496 (125 – 835) Increasing 2.1 (0.9 – 3.3) 4.2 (2.2 – 42)
Poland 282 (116 – 439) Likely increasing 1.5 (0.9 – 2.1) 9 (4.1 – Cases decreasing)
Portugal 904 (390 – 1385) Likely increasing 1.4 (0.9 – 2) 9.2 (4.2 – Cases decreasing)
Qatar 55 (16 – 96) Likely increasing 1.9 (0.8 – 2.9) 5.3 (2.6 – Cases decreasing)
Romania 301 (132 – 465) Likely increasing 1.6 (1 – 2.2) 7.8 (3.8 – Cases decreasing)
Russia 356 (128 – 557) Increasing 1.8 (0.9 – 2.6) 5.6 (2.9 – 81)
Saudi Arabia 160 (49 – 265) Unsure 1.2 (0.8 – 1.6) 29 (5.9 – Cases decreasing)
Serbia 166 (44 – 281) Likely increasing 2 (0.8 – 3.1) 4.8 (2.3 – Cases decreasing)
Singapore 84 (30 – 124) Likely increasing 1.4 (0.9 – 1.9) 10 (4.3 – Cases decreasing)
South Africa 132 (54 – 216) Unsure 1.1 (0.6 – 1.6) 55 (5.2 – Cases decreasing)
South Korea 126 (66 – 181) Unsure 1.1 (0.8 – 1.5) 26 (7.4 – Cases decreasing)
Spain 8770 (5252 – 12784) Likely increasing 1.2 (0.9 – 1.6) 16 (6 – Cases decreasing)
Sweden 389 (192 – 594) Likely increasing 1.4 (0.9 – 1.9) 10 (4.5 – Cases decreasing)
Switzerland 1448 (749 – 2188) Likely increasing 1.3 (0.8 – 1.7) 19 (6.4 – Cases decreasing)
Thailand 182 (64 – 286) Likely increasing 1.3 (0.8 – 1.9) 21 (5.2 – Cases decreasing)
Turkey 3068 (780 – 4827) Likely increasing 2.1 (0.9 – 3.1) 4.3 (2.2 – 45)
Ukraine 135 (25 – 228) Likely increasing 2.2 (0.7 – 3.5) 4.3 (2.2 – Cases decreasing)
United Arab Emirates 91 (22 – 162) Likely increasing 1.6 (0.9 – 2.2) 8.1 (3.1 – Cases decreasing)
United Kingdom 3137 (1707 – 4526) Increasing 1.5 (1 – 2.1) 7.7 (4 – 54)
United States of America 22748 (13251 – 32240) Increasing 1.4 (1 – 1.8) 9.2 (4.8 – 100)

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